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Description

Adverse events between police and the public, such as deadly shootings or instances of racial proﬁling, can cause serious or deadly harm, damage police legitimacy, and result in costly litigation. Evidence suggests these events can be prevented by targeting interventions based on an Early Intervention System (EIS) that ﬂags police oﬃcers who are at a high risk for involvement in such adverse events. Today’s EIS are not data-driven and typically rely on simple thresholds based entirely on expert intuition. In this paper, we de-scribe our work with the Charlotte-Mecklenburg Police Department (CMPD) to develop a machine learning model to predict which oﬃcers are at risk for an adverse event. Our approach signiﬁcantly outperforms CMPD’s existing EIS, increasing true positives by ∼ 12% and decreasing false positives by ∼ 32%. Our work also sheds light on features related to oﬃcer characteristics, situational factors, and neighborhood factors that are predictive of adverse events. This work provides a starting point for police departments to take a comprehensive, data-driven approach to improve policing and reduce harm to both oﬃcers and members of the public.

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